# devtools::install_github("derekmichaelwright/agData")
library(agData) # Loads: tidyverse, ggpubr, ggbeeswarm, ggrepel
# 
myCaption <- "www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: STATCAN"
myAreas <- c("Canada", "British Columbia", "Alberta", "Saskatchewan", "Manitoba", 
             "Ontario", "Quebec", "New Brunswick", "Prince Edward Island", 
             "Nova Scotia", "Newfoundland and Labrador",  
             "Yukon", "Northwest Territories", "Nunavut")
myAs <- c("CA", "BC", "AB", "SK", "MB", "ON", "QC", "NB",
          "PE", "NS", "NL", "YT", "NT", "NU")
myCs <- c("black", "darkgoldenrod3", "darkred", "darkgreen", "darkslategray", 
          "darkblue", "steelblue", "maroon4", "purple4", 
          "cyan4", "burlywood4", "green4", "darkgoldenrod2", "red3")
#
myPal <- leaflet::colorFactor(palette = myCs, domain = myAreas)
# GDP by province
d1 <- read.csv("3610040201_databaseLoadingData.csv") %>%
  select(-Value) %>%
  rename(Area=GEO, Year=REF_DATE, Value=VALUE, 
         Industry=North.American.Industry.Classification.System..NAICS.) %>%
  mutate(Value = Value * 1000000,
         Area = factor(Area, levels = myAreas),
         Area_Short = plyr::mapvalues(Area, myAreas, myAs),
         Color = plyr::mapvalues(Area, myAreas, myCs)
         ) %>%
  select(Area, Area_Short, Color, Year, Industry, Value)
# GDP percent by industry
d2 <- read.csv("3610040001_databaseLoadingData.csv") %>%
  rename(Area=GEO, Year=REF_DATE, Value=VALUE,
         Industry=North.American.Industry.Classification.System..NAICS.) %>%
  mutate(Area = factor(Area, levels = myAreas),
         Area_Short = plyr::mapvalues(Area, myAreas, myAs)) %>%
  select(Area, Area_Short, Year, Industry, Value)
d2.1 <- d2 %>% group_by(Year, Industry) %>%
  summarise(Value = sum(Value, na.rm = T)) %>%
  ungroup() %>%
  mutate(Area = "Canada", AreaShort = "CA")
d2 <- bind_rows(d2, d2.1)
# Population by province
d3 <- read.csv("1710000901_databaseLoadingData.csv") %>% 
  rename(Year=REF_DATE, Area=GEO) %>%
  mutate(Month = as.numeric(substr(Year, 6, 7)),
         Year = as.numeric(substr(Year, 1, 4))) %>%
  filter(Month == 1) %>%
  rename(Number = VALUE)

# Prep data
industries <- c("Goods-producing industries [T002]",
                "Service-producing industries [T003]")
xx <- d2 %>% filter(Industry %in% industries) %>%
  mutate(Industry = factor(Industry, levels = rev(industries)))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, fill = Industry)) +
  geom_col(alpha = 0.7) +
  facet_wrap(Area ~ ., ncol = 5, scales = "free_y") +
  scale_fill_manual(name = NULL, values = c("darkred", "darkgreen")) +
  theme_agData(legend.position = "bottom") +
  labs(title = "GDP by Industry", x = NULL,
       y = "Percent", caption = myCaption)
ggsave("canada_gdp_09.png", mp, width = 10, height = 6)

ggGDP_Areas <- function(year, areas, colors) {
  industries <- c("Information and communication technology sector [T013]",
                  "Energy sector [T016]", "Industrial production [T010]",
                  "Goods-producing industries [T002]",
                  "Service-producing industries [T003]",
                  "All industries [T001]")
  # Prep data
  xx <- d2 %>%
    filter(Year == year, Area %in% areas, !Industry %in% industries) %>%
    left_join(d3, by = c("Area", "Year")) %>%
    mutate(GDP_PP = 1000000 * Value / Number) %>%
    filter(!is.na(GDP_PP))
  levs <- xx %>% filter(Year == year, Area == areas[1]) %>% 
    arrange(Value) %>% pull(Industry)
  xx <- xx %>% 
    mutate(Industry = factor(Industry, levels = levs),
           Area = factor(Area, levels = areas))
  # Plot
  ggplot(xx, aes(x = Industry, y = GDP_PP, fill = Area)) +
    geom_col(position = "dodge", width = 0.75,
             alpha = 0.7, color = "black", lwd = 0.3) +
    scale_fill_manual(name = NULL, values = colors) +
    theme_agData(legend.position = "bottom") +
    coord_flip() +
    labs(title = paste("GDP Per Person", year), x = NULL,
         y = "Million $ / Person", caption = myCaption)
}

ggGDP_Years <- function(area, years, colors) {
  industries <- c("Information and communication technology sector [T013]",
                  "Energy sector [T016]", "Industrial production [T010]",
                  "Goods-producing industries [T002]",
                  "Service-producing industries [T003]",
                  "All industries [T001]",
                  "Finance and insurance, real estate and renting and leasing and management of companies and enterprises")
  # Prep data
  xx <- d2 %>% 
    filter(Year %in% years, Area == area, !Industry %in% industries) %>%
    left_join(d3, by = c("Area", "Year")) %>%
    mutate(GDP_PP = 1000000 * Value / Number) %>%
    filter(!is.na(GDP_PP))
  levs <- xx %>% filter(Year %in% years[1], Area == area) %>% 
    arrange(Value) %>% pull(Industry)
  xx <- xx %>% 
    mutate(Industry = factor(Industry, levels = levs),
           Year = factor(Year)
           #Area = factor(Area, levels = areas)
           )
  # Plot
  ggplot(xx, aes(x = Industry, y = GDP_PP, fill = Year)) +
    geom_col(position = "dodge", width = 0.75,
             alpha = 0.7, color = "black", lwd = 0.3) +
    scale_fill_manual(name = NULL, values = colors) +
    theme_agData(legend.position = "bottom") +
    coord_flip() +
    labs(title = paste("GDP Per Person", area), x = NULL,
         y = "Million $ / Person", caption = myCaption)
}

mp <- ggGDP_Years(area = "Canada",
                  years = c(2019, 2022), 
                  colors = c("steelblue","darkred"))
ggsave("canada_gdp_16.png", mp, width = 8, height = 6)

mp <- ggGDP_Years(area = "Canada",
                  years = c(2008, 2015, 2019, 2022), 
                  colors = c("steelblue","darkblue","red3","darkred"))
ggsave("canada_gdp_17.png", mp, width = 8, height = 6)